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Found 21 papers, 8 papers with code
SIGMORPHON 2021 Shared Task on Morphological Reinflection: Generalization Across Languages
This year's iteration of the SIGMORPHON Shared Task on morphological reinflection focuses on typological diversity and cross-lingual variation of morphosyntactic features.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
UniMorph 4.0: Universal Morphology
The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema.
Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP
What are the units of text that we want to model?
SIGTYP 2021 Shared Task: Robust Spoken Language Identification
While language identification is a fundamental speech and language processing task, for many languages and language families it remains a challenging task.
Reducing conversational agents' overconfidence through linguistic calibration
While improving neural dialogue agents' factual accuracy is the object of much research, another important aspect of communication, less studied in the setting of neural dialogue, is transparency about ignorance.
SIGTYP 2020 Shared Task: Prediction of Typological Features
Typological knowledge bases (KBs) such as WALS (Dryer and Haspelmath, 2013) contain information about linguistic properties of the world's languages.
Processing South Asian Languages Written in the Latin Script: the Dakshina Dataset
This paper describes the Dakshina dataset, a new resource consisting of text in both the Latin and native scripts for 12 South Asian languages.
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection
Systems were developed using data from 45 languages and just 5 language families, fine-tuned with data from an additional 45 languages and 10 language families (13 in total), and evaluated on all 90 languages.
It's Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information
The performance of neural machine translation systems is commonly evaluated in terms of BLEU.
UniMorph 3.0: Universal Morphology
The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema.
Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!
Topic models are a useful analysis tool to uncover the underlying themes within document collections.
The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection
The SIGMORPHON 2019 shared task on cross-lingual transfer and contextual analysis in morphology examined transfer learning of inflection between 100 language pairs, as well as contextual lemmatization and morphosyntactic description in 66 languages.
What Kind of Language Is Hard to Language-Model?
Trying to answer the question of what features difficult languages have in common, we try and fail to reproduce our earlier (Cotterell et al., 2018) observation about morphological complexity and instead reveal far simpler statistics of the data that seem to drive complexity in a much larger sample.
Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology
Gender stereotypes are manifest in most of the world's languages and are consequently propagated or amplified by NLP systems.
UniMorph 2.0: Universal Morphology
The Universal Morphology UniMorph project is a collaborative effort to improve how NLP handles complex morphology across the world's languages.
The CoNLL--SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection
Apart from extending the number of languages involved in earlier supervised tasks of generating inflected forms, this year the shared task also featured a new second task which asked participants to inflect words in sentential context, similar to a cloze task.
Unsupervised Disambiguation of Syncretism in Inflected Lexicons
Lexical ambiguity makes it difficult to compute various useful statistics of a corpus.
Are All Languages Equally Hard to Language-Model?
For general modeling methods applied to diverse languages, a natural question is: how well should we expect our models to work on languages with differing typological profiles?
A Structured Variational Autoencoder for Contextual Morphological Inflection
Statistical morphological inflectors are typically trained on fully supervised, type-level data.
Spell Once, Summon Anywhere: A Two-Level Open-Vocabulary Language Model
By invoking the second RNN to generate spellings for novel words in context, we obtain an open-vocabulary language model.
